photoediter

里面涉及了线宽线型的设置，各种图形，还有画笔画刷设置选择，颜色设置等，代码非常齐全，还有编程的每一步都有截图和说明(Which involves the linewidth linear settings, a variety of graphics, and brush brush settings selection, color settings, the code is very complete, and programming of every step have screenshots and description)

该代码采用opencv环境来实现灰度投影估计电子稳像算法，能够很好地解决视频序列抖动问题。(The code uses the opencv environment to achieve the gray projection algorithm, which can solve the problem of video sequence jitter.)

用hough变换提取直线的C++代码，效果比较好，可以灵活改变可提取直线的条数，适用于提取多条直线。(line detecting using hough tranaformation)

说明：  双目测距bm算法，已经实现,基于vs2015+opencv3.2,测距精度很高，一米内精度一厘米误差(Binocular ranging bm algorithm, which has been implemented, is based on vs2015 + opencv3.2, which has high accuracy and one centimeter error in one meter)

源点定位程序，可用于检测连通域等功能，代码是c/c++的，放心使用。(Source point positioning procedures, can be used to detect other functions connected domain, the code is c/c++, and ease of use.)

RAw格式图片的显示，边缘检测，包括SOBEL算子，具有不同模板的拉普拉斯算子边缘检测，以及一些简单的滤波方式(sadfsadf asdfasdf asdf)

说明：  基于Kmeans算法的图像分割，一般Kmeans是数据挖掘中用来聚类的，本试验利用图像中的灰度值作为Kmeans算法的原始点，进行图像分割(Kmeans algorithm based on image segmentation, data mining in general Kmeans is used to clustering, the trial use of the image gray value as the original algorithm Kmeans spots for Image Segmentation)

radon算法opencv实现，能够很好的实现图像的水平旋转矫正。( implementing the radon by opencv.)

说明：  基于opencv的小面积区域的去除.可以去除面积小于指定面积的无兴趣区域.(Opencv-based small area of the region to remove. Can remove an area of less than the specified area is not interested in the region.)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)